Systems and methods for detecting and reporting anomalies in utility meters

ABSTRACT

Systems and methods for utility intervention are disclosed. The method of utility intervention includes: (i) obtaining utility data from a utility data repository; (ii) detecting, using at least one type of anomaly-detecting module, at least one utility anomaly and a location address; (iii) calculating an amount of financial savings for the utility anomaly if the utility anomaly was remedied or addressed; (iv) computing a certainty score for the utility anomaly; (v) conveying information about the type of utility anomaly, and the location address of the utility anomaly; and (vi) displaying, on a display screen of a client device, a map depicting a geographical area that identifies, using a flag icon, the location address on the map of the utility anomaly, the type of the utility anomaly, a certainty score for the utility anomaly, and/or an amount of financial savings associated with the utility anomaly.

RELATED APPLICATION

The present application claims priority to U.S. provisional applicationNo. 62/478,831, with a filing date of Mar. 30, 2017, which isincorporated herein by reference in its entirety for all purposes.

FIELD

The present teachings and arrangement relate generally to systems andmethods for detecting and reporting anomalies in, or related to, utilitydata meters. More particularly, the present teachings and arrangementsrelate to systems and methods that analyze water utility data streamsfrom, for example, a utility company and/or external data streams fromother sources to detect anomalies in water utility meters installed at aparticular location.

BACKGROUND

Water utilities companies serve local communities and deliver water totheir customers, which may be individuals, commercial entities,government entities, non-profit entities, or the like. Water utilitiestrack water use by their customers using water utility meters, installedat or near a location associated with customers, and which measure waterconsumption at that location. Based in part on such measurements ofwater consumption, a customer is invoiced by the water utility company.

As a byproduct of providing water to their customers, multiple datastreams are generated, collected, and/or otherwise available to thewater utility companies. These data streams include, but are not limitedto, water utility meter data (i.e., data associated with a water meter'smeasurement of water use at a location); water utility billing data(i.e., data associated with amounts a customer is invoiced for water useat a location), and/or external data (i.e., other types of data, notfrom a utility company, but related to water use at the customer'slocation, e.g., climate and weather data, water quality data, orproperty data, such as square footage, zoning, building age, number ofbedrooms at a building on a location, number of bathrooms at a buildingon a location, number of swimming pools on a particular location, andassessed property value).

Water meters used by water utilities to measure water use at a locationoften suffer from certain problems that affect water consumption, watermeasurement, and/or water billing for a particular location. Forexample, water meters are often too small or too large relative to anamount of water being supplied to a location; water meters are oftenmisclassified based on the class of use at a location (e.g., commercial,residential, or industrial); water meters sometimes under-register anamount of water being delivered to a location; and/or water meters at alocation are often tampered with in ways that affect water use,measurement, and/or billing at a location.

Unfortunately, conventional systems and techniques for tracking and/orbilling for water consumption lack means to detect anomalies associatedwith utility water meters (i.e., utility anomalies) that reveal suchproblems to the water utility company, such that the water utilitycompany may take steps to remediate these defective water utilitymeters. In particular, conventional techniques fail to make sufficientuse of the available data streams to identify anomalies associated withwater utility meters that require adjustment, repair, or replacement,and to optimize or otherwise improve measurement of water use and/orwater billing at a location.

What is, therefore, needed are novel systems and methods that use datastreams available to water utility companies to detect anomalies inwater meters, to report such anomalies and related information to thewater utility company and/or customer, and to prompt further action bythe water utility company to reclassify, adjust, repair, and/or replacewater utility meters.

SUMMARY

To achieve the foregoing, the present teachings and arrangements providesystems and methods for utility intervention, which identifies a utilityanomaly by analyzing utility data. Once detected, anomalies and/orrelated information may be reported to the water utility company and/orcustomer, prompting a water utility company and/or a third-party workerto remediate the water meter producing such anomalies, thus providingcertain benefits, including cost savings and energy savings, to thewater utility company and/or customer.

In one aspect, the present teachings disclose a method of utilityintervention. An exemplar method, of this aspect, includes: (i)obtaining utility data from a utility data repository; (ii) detecting,using at least one type of anomaly-detecting module installed on aserver, one or more utility anomalies of at least one type and alocation address of one or more of the utility anomalies; (iii)calculating, using the server, an amount of financial savings for atleast one of the utility anomalies if the utility anomaly was remediedor addressed, so that said utility anomaly was no longer deemed ananomaly by the server; (iv) computing, using the server, a certaintyscore for at least one of the utility anomalies, where the certaintyscore is a measure of certainty that the utility anomaly, obtained fromdetecting, is indeed an anomaly, and not a false positive result; (v)conveying the certainty score for at least one of the utility anomalies,information about the type of one or more of the utility anomalies, andthe location address of one or more of the utility anomalies from theserver to a client device, which is communicatively coupled to theserver; and (vi) displaying, on a display screen of the client device, amap depicting a geographical area that identifies, using a flag icon, atleast one of the location address on the map of one or more of theutility anomalies, the type of at least one of the utility anomalies, acertainty score for each of the utility anomalies, and/or an amount offinancial savings associated with each of the utility anomalies.

According to one embodiment of the present teachings, the method ofutility intervention includes using a data transformer module totransform the utility data obtained from the utility data repositoryinto an acceptable form, which allows the above-mentioned step ofdetecting to be carried out. To produce this acceptable form, forexample, the location addresses in the utility data may be converted tothe same format, and/or timestamps in the utility data may be convertedto time values in the same time zone. Preferably, the utility data inacceptable form is stored in a data storage device. Then, the utilitydata in acceptable form may be obtained from and/or accessed in the datastorage device, to the server, to carry out the above-mentioned step ofdetecting.

According to preferred embodiments of the present teachings, theanomaly-detecting module, used during the above-mentioned step ofdetecting, includes at least one module chosen from a group comprisingmeter under-sizing detector, meter over-sizing detector, metermisclassification detector, meter tampering detector, and meterunder-registration detector. The meter under-sizing detector may detectwhether a utility meter at the location address has a size smaller thana predetermined size for the utility meter. The meter over-sizingdetector may detect whether the utility meter at the location addresshas a size larger than the predetermined size for said utility meter.The meter misclassification detector may detect whether the utilitymeter at the location address is misclassified. The meter tamperingdetector may detect whether the utility meter at the location addresshas been tampered with. The meter under-registration detector may detectwhether the utility meter at the location address is under registeringthe amount of use of the utility at the location address. Preferably, autility meter measures an amount of use of the utility (e.g., water) atthe location address.

According to one preferred embodiment of the present teachings, theabove-mentioned step of conveying includes sending the certainty scorefor at least one of the utility anomalies and the information about thetype of one or more of the utility anomalies, from the server to memory,and then preferably, from memory, to a data reporter.

The location address may convey information about a boundary of ahabitable area and information about an external area that is outsidethe habitable area. According to one embodiment of the presentteachings, the external area conveys qualitative information about anature of use of the location address and/or about an extent ofconsumption of the utility due to the nature of the external area, suchthat the qualitative information allows user of the client device todeduce an extent of consumption of the utility in the habitable area.

In certain embodiments of the present teachings, the flag is presentedas a non-selectable icon on the user interface of the client device. Inother embodiments of the present teachings, however, the flag ispresented as a selectable icon on the user interface of the clientdevice. Preferably, upon the user's selection of the selectable icon forthe flag, information regarding remediation of the utility anomaly ispresented. This may include providing instructions for remediating theutility anomaly. This may also include presenting an input region, onthe user interface, for receiving remediation information of the utilityanomaly.

The method of utility intervention may also include transmitting theremediation information, through a text or an electronic mail addressassociated with the location address, to present one or more availabletimes to perform remediation at the location address. The method ofutility intervention may further include receiving one or more selectedavailable times, for remediation at the location address, from the textor the electronic mail address associated with the location address. Themethod of utility intervention may further still include conveying theselected available times, to carry out remediation at the locationaddress, to a remediation entity or worker. The method of utilityintervention may further still include transforming the display of theflag from a selectable icon to a non-selectable icon and/or transmittingnotice, through a text or an electronic email address associated withthe location address, that the remediation entity or worker hascompleted the remediation at the location address.

The method of utility intervention may further include transmitting anestimated value for cost savings, at the location address, resultingfrom the remediation at the location address, to the client device.Further, a billing statement associated with the location address mayprovide an estimated value of cost savings, at the location address,resulting from the remediation at the location address.

Systems and methods of the present teachings and arrangements, however,together with additional objects and advantages thereof, will be bestunderstood from the following descriptions of specific embodiments whenread in connection with the accompanying figures.

BRIEF DESCRIPTIONS OF THE DRAWINGS

FIG. 1 is a block diagram of a system, according to one embodiment ofthe present arrangements, for detecting and reporting utility anomaliesassociated with utility data and/or external data, where systemcomponents are depicted within solid lines, and non-system componentsthat deliver inputs to, and receive outputs from, the system aredepicted within dashed lines.

FIG. 2A is a block diagram showing a data receptor, according to oneembodiment of the present arrangements, and depicting its relationshipsto certain inputs, outputs, and non-system devices associated with thepresent systems for detecting utility anomalies (e.g., system 100 ofFIG. 1 ).

FIG. 2B is a block diagram showing a data receptor, according to analternate embodiment of the present arrangements, and depicting itsrelationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 3A is a block diagram showing a data transformer, according to oneembodiment of the present arrangements, and depicting its relationshipsto certain inputs, outputs, and non-system devices associated with thepresent systems for detecting utility anomalies (e.g., system 100 ofFIG. 1 ).

FIG. 3B is a block diagram showing a data transformer, according to analternate embodiment of the present arrangements, and depicting itsrelationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 4A is a block diagram showing a meter under-sizing detector module,according to one embodiment of the present arrangements, and depictingits relationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 4B is a block diagram showing a meter under-sizing detector module,according to an alternate embodiment of the present arrangements, anddepicting its relationships to certain inputs, outputs, and non-systemdevices associated with the present systems for detecting utilityanomalies (e.g., system 100 of FIG. 1 ).

FIG. 4C is a block diagram showing a meter under-sizing detector module,according to another alternate embodiment of the present arrangements,and depicting its relationships to certain inputs, outputs, andnon-system devices associated with the present systems for detectingutility anomalies (e.g., system 100 of FIG. 1 ).

FIG. 5A is a block diagram showing a meter over-sizing detector module,according to one embodiment of the present arrangements, and depictingits relationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 5B is a block diagram showing a meter over-sizing detector module,according to an alternate embodiment of the present arrangements, anddepicting its relationships to certain inputs, outputs, and non-systemdevices associated with the present systems for detecting utilityanomalies (e.g., system 100 of FIG. 1 ).

FIG. 5C is a block diagram showing a meter over-sizing detector module,according to another alternate embodiment of the present arrangements,and depicting its relationships to certain inputs, outputs, andnon-system devices associated with the present systems for detectingutility anomalies (e.g., system 100 of FIG. 1 ).

FIG. 6A is a block diagram showing a meter misclassification detectormodule, according to one embodiment of the present arrangements, anddepicting its relationships to certain inputs, outputs, and non-systemdevices associated with the present systems for detecting utilityanomalies (e.g., system 100 of FIG. 1 ).

FIG. 6B is a block diagram showing a meter misclassification detectormodule, according to an alternate embodiment of the presentarrangements, and depicting its relationships to certain inputs,outputs, and non-system devices associated with the present systems fordetecting utility anomalies (e.g., system 100 of FIG. 1 ).

FIG. 7A is a block diagram showing a meter tampering detector module,according to one embodiment of the present arrangements, and depictingits relationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 7B is a block diagram showing a meter tampering detector module,according to an alternate embodiment of the present arrangements, anddepicting its relationships to certain inputs, outputs, and non-systemdevices associated with the present systems for detecting utilityanomalies (e.g., system 100 of FIG. 1 ).

FIG. 8A is a block diagram showing a meter under-registration detectormodule, according to one embodiment of the present arrangements, anddepicting its relationships to certain inputs, outputs, and non-systemdevices associated with the present systems for detecting utilityanomalies (e.g., system 100 of FIG. 1 ).

FIG. 8B is a block diagram showing a meter under-registration detectormodule, according to an alternate embodiment of the presentarrangements, and depicting its relationships to certain inputs,outputs, and non-system devices associated with the present systems fordetecting utility anomalies (e.g., system 100 of FIG. 1 ).

FIG. 9A is a block diagram showing a data reporter, according to oneembodiment of the present arrangements, and depicting its relationshipsto certain inputs, outputs, and non-system devices associated with thepresent systems for detecting utility anomalies (e.g., system 100 ofFIG. 1 ).

FIG. 9B is a block diagram showing a data reporter, according to analternate embodiment of the present arrangements, and depicting itsrelationships to certain inputs, outputs, and non-system devicesassociated with the present systems for detecting utility anomalies(e.g., system 100 of FIG. 1 ).

FIG. 10 is an illustration of a user interface, according to oneembodiment of the present arrangements and that shows a map with anon-selectable flag icon and a dialog box.

FIG. 11 is a diagram of another user interface, according to analternate embodiment of the present arrangements and that shows a mapwith a selectable flag icon and a dialog box.

FIG. 12A is a diagram of yet another user interface, according toanother alternate embodiment of the present arrangements and that showsa dialog box for entering instruction to a worker.

FIG. 12B is a diagram of yet another user interface, according toanother embodiment of the present arrangements and that shows the dialogbox of FIG. 12A adjacent to a different dialog box that is used forreceiving remarks or comments from a worker regarding remediation of oneor more utility anomalies.

FIG. 13 is a process flow diagram showing certain salient steps of aprocess, according to one embodiment of the present teachings, forutility intervention.

DETAILED DESCRIPTION OF THE INVENTION

The present teachings and arrangements relate to systems and methods ofusing utility and/or external data streams to detect utility anomaliesthat a utility company and/or utility customer may be interested inremediating.

Water utilities provide services to a variety of customer classes,including residential, commercial, government, and industrial. Waterutilities may be standalone entities or can exist within a largerorganization offering other services, including electric or gas. Waterutilities may be public or private entities.

Water utilities deliver water to their customers in exchange for feesthat comprise revenue to the utility. Water utilities may collectrevenue from a variety of fees. Some fees are flat, non-metered fees,without regard to how much of the service is used by the customer. Otherfees are metered based on the quantity of service, such as water, thatis consumed.

Water utilities calculate the amount that each customer owes, and sendeach customer an invoice. Invoices may be sent on a recurring basis. Thecalculated fees can take into account the amount of water registered bya water meter installed at the customer's property, or location. Thecalculated fees may also take into account information about the watermeter that has been installed, such as the size and type of the meter.Moreover, the calculated fees may also take into account informationabout the customer, such as whether the customer is classified asresidential, commercial, or as another class of customer. For example,residential water utility customers may pay a different rate thancommercial water utility customers, whether or not they have consumed adifferent amount of water. In some cases, if water utilities useincorrect information in calculating the amount that a customer owes,they may invoice the customer the wrong amount. Water utilities may beinterested in identifying situations when incorrect invoices have beensent out. They can use the information to fix incorrect invoices, eitherin the past (retroactively), or in the future.

The data generated through the process of delivering water to customersmay be used to identify utility anomalies that that reveal certainproblems or defects associated with water meters, such as meterover-sizing, meter under-sizing, meter misclassification, meterunder-registration, and/or meter tampering. Such problems may alsoindicate that a past, present, or future invoice is incorrect, and/orthat water use at a location is not being accurately measured.

As explained in further detail below, systems of the presentarrangements include a set of data receptors, data transformers,anomaly-detecting modules, and data reporters. The data receptors anddata transformers collectively provide data streams as inputs toanomaly-detecting modules. Each anomaly-detecting module operates on oneor more data streams and produces a list of utility anomalies asoutputs. The data reporters provide information about utility anomaliesto a user. Each utility anomaly may be based on water utility meterdata, water utility billing data, external data, or a combination ofmultiple types of data.

FIG. 1 is a block diagram showing certain salient components of a system100, according to one embodiment of the present arrangements, fordetecting and reporting utility anomalies. Components in FIG. 1 depictedwithin solid boundary lines represent components of the present systemsfor detecting and reporting utility anomalies, while components and/orobjects (e.g., data) depicted within dashed boundary lines representnon-system components and/or objects that provide inputs to and/orreceive outputs from the present systems for detecting and reportingutility anomalies.

FIG. 1 includes a water meter 102, a water utility meter data repository104, a water utility billing data repository 106, a first external datarepository 108, a second external data repository 110, a data receptor112, a data transformer 114, anomaly-detecting modules 116 (whichinclude such modules as a meter under-sizing detector module 118, ameter over-sizing detector module 120, a meter misclassificationdetector module 122, a meter tampering detector module 124, and a meterunder-registration detector module 126), a data reporter 128, a user130, a data storage device A 132, a data storage device B 134, and adata storage device C 136.

A utility may be any utility (e.g., water, electricity, or natural gas)used at a location (e.g., a personal residence or commercial property)where consumption of the utility is measured by a meter. Preferably, autility is water, with use of the water measured at a location addressby a water utility meter installed by a water utility company at or nearthat location address.

A user (e.g., user 130) is a person or entity to whom information aboutone or more utility anomalies is delivered (preferably via a clientdevice associated with user 130). The user may be someone at a locationaddress and/or user of a client device. For example, user 130 may be awater utility company, a customer, and/or a third party worker hired toremediate a water utility meter that produces anomalies.

Water meter 102 is a water utility meter, installed at or near acustomer's location, for measuring, recording, and/or registering anamount of water used at the customer's location over a period of time.The quantity of water used at the customer, during a period of time, maybe obtained by periodically reading the meter. The meter may be readeither automatically or manually. The data describing the quantity ofwater used, i.e., water utility meter data, may be generated by theutility, by one or more third-party providers, or by a combination ofthe utility and one or more third-party providers.

Water meter 102 is communicatively coupled to water utility meter datarepository 104 such that water meter 102 delivers water utility meterdata streams to water utility meter data repository 104 for storing andfor downstream conveyance of the water utility meter data streams intothe present systems for detecting and reporting utility anomalies. Asused herein, “communicatively coupled” includes being connected via anetwork, such as the Internet, an intranet, a cellular network, or awireless network, as well as being linked by a direct connection (e.g.,a wired connection).

As shown in FIG. 1 , other types of data repositories may store othertypes of data as inputs to the present systems for detecting andreporting utility anomalies. For example, water utility billing datarepository 106 stores water utility billing data. Water utility billingdata reflects the amount of money a customer owes to a water utility inexchange for the utility providing water service. Water utility billingdata may take into account the quantity of water consumed during aperiod of time, in conjunction with other aspects of the customer (e.g.,class of customer, location type, meter size, or meter type). Waterutility billing data may be generated by the water utility's billingdepartment, by one or more third-party providers, or by a combination ofthe utility and one or more third-party providers.

System 100 of FIG. 1 also accounts for external sources of data—otherthan data from a water utility company—that may be used in the presentsystems for detecting and reporting utility anomalies (i.e., “externaldata”). To this end, first external data repository 108 and secondexternal data repository 110 each store external data, from third-partysources, that may be related to water meter 102, a customer's property,a location where a water meter has been installed, or another attributethat is related to a water utility customer and/or use of a waterservice. External data may include, but is not limited to, climate andweather data, water quality data, or property data (e.g., squarefootage, zoning, building age, number of bedrooms, number of bathrooms,or assessed property value). Though the embodiments of FIG. 1 show twoexternal data repositories 108 and 110, the present arrangementscontemplate use of any number of external data repositories necessary tostore external data and to provide external data stream as inputs to thepresent systems for detecting and reporting utility anomalies.

Each of water utility meter data repository 104, water utility billingdata repository 106, first external data repository 108, and secondexternal data repository 110, is communicatively coupled with datareceptor 112, such that each data repository conveys associated datastreams (e.g., water utility meter data stream, water utility billingdata stream, a first external data stream, and a second external datastream, respectively) as inputs to data receptor 112, for downstream usein the present systems for detecting and reporting utility anomalies.

Data receptor 112 is communicatively coupled to data storage device A132, such that data streams received and/or accessed by data receptor112, from water utility meter data repository 104, water utility billingdata repository 106, first external data repository 108, and secondexternal data repository 110, are housed in data storage device A 132.According to one embodiment of the present arrangements, data receptor112 is a computer or computer component that copies water utility dataand/or external data from one computer system (e.g., a computer systemassociated with water utility meter data repository 104, water utilitybilling data repository 106, first external data repository 108, andsecond external data repository 110) to a data storage device (e.g.,data storage device A 132 of FIG. 1 ).

Data storage device A 132 is a component that stores one or more datastreams (e.g., water utility data streams and/or external data streams)received from or accessed by data receptor 112. According to oneembodiment of the present arrangements, data storage device A 132includes at least one member selected from a group comprising computerdisk drive, RAM, magnetic tape, magnetic drive, magnetic disk, flashmemory, cloud storage, optical disk, and cache memory. As shown in FIG.1 , data storage device A 132 is communicatively coupled to datatransformer 114.

Data transformer 114 is a computer or computer component that is capableof transforming, or modifying, water utility data and/or external datareceived from data storage device A. Data transformer 114 thus mayinclude a processor capable of carrying out conversion of unmodifieddata to modified data that is used by downstreamanomaly-detecting-modules (explained below) to detect the existence ofutility anomalies. Modified or transformed data may be thought of asdata that is in an “acceptable form” that facilitates detection of oneor more utility anomalies by downstream components of system 100.

Data transformer 114 is communicatively coupled to data storage device B134, such that data transformer 114 delivers modified or transformeddata to data storage device B 134 for storage. Data storage device B 134is communicatively coupled to anomaly-detecting modules 116, such thatmodified or transformed data is conveyed to one or more ofanomaly-detecting modules 116 for processing (i.e., for detection of onemore anomalies).

According to preferred embodiments of the present arrangements, each ofanomaly-detecting modules 116 is a module configured to search modifiedwater utility data and/or modified external data in data storage deviceB 134 for the existence of and/or nature of one or more utilityanomalies. In certain embodiments of the present arrangements, any ofanomaly-detecting modules 116 is configured to perform calculations,using modified utility data and/or modified external data, and one ormore predetermined threshold values, to detect the existence of and/ornature of one or more utility anomalies. For example, meter under-sizingdetector module 118 detects whether water utility meter 102 has a sizethat is smaller than a predetermined size for the utility meter. Asanother example, meter over-sizing detector module 120 detects whetherutility meter 102 has a size that is larger than a predetermined sizefor the water meter. As yet another example, meter misclassificationdetector module 122 detects whether utility meter 102 is misclassified(e.g., misclassified as being used in a commercial setting, when it isin fact being used in a residential setting, or vice versa). As yetanother example, meter tampering detector module 124 detects whetherwater utility meter 102 has been tampered with or adjusted in a way thatrequires correction. Finally, as yet another example, meterunder-registration detector 126 is a module that detects whether waterutility meter 102 is under-registering an amount of use of water at alocation associated with water utility meter 102. The present teachingsrecognize that any one or more of anomaly-detecting modules 118, 120,122, or 124, 126, may be used by the systems of the present arrangementsto detect one or more utility anomalies. Further, in other embodimentsof the present arrangements, one or more other types ofanomaly-detecting modules are used to detect the existence of any typeof utility anomaly or other types of anomalies, which may be associatedwith a utility meter.

Each of anomaly-detecting modules 116 is communicatively coupled to datastorage device C 136 such that data storage device C 136 receives as aninput, and stores, a list of utility anomalies detected by one or moremodules inside anomaly-detecting modules 116.

Data storage device C is communicatively coupled to data reporter 128.Data reporter 128 is a computer or computer component that allows a userto view information about the detected utility anomalies. In certainembodiments of the present arrangements, data reporter 128 sendsinformation about one or more utility anomalies to a client devicehaving a user interface. As explained in further detail below withreference to FIGS. 10-12B, a client device may be used to access andpresent certain additional information, related to one or more utilityanomalies, that guide or prompt further action on the part of a userand/or a water utility company, including remediation of the waterutility meter that produces one or more of the undesired utilityanomalies.

While the embodiment of FIG. 1 shows three data storage devices (i.e.,data storage device A 132, data storage device B 134, and data storagedevice C 136, systems of the present arrangement may include any numberof data storage devices. According to one embodiment of the presentarrangements, data storage device A 132, data storage device B 134, anddata storage device C 136, are the same. According to another embodimentof the present arrangements, data storage device A 132 and data storagedevice 134 B are the same. According to yet another embodiment of thepresent arrangements, data storage device A 132 and data storage deviceC 136 are the same. According to yet another embodiment of the presentarrangement, data storage device B 134 and data storage device 136 C arethe same. Any number of data storage devices, including one data storagedevice, may be employed by systems of the present arrangements.

System 100 of FIG. 1 may be configured to receive new inputs (e.g., datastreams) that are repeatedly fed into the system, either on a regular,recurring basis, or on an occasional, as-needed basis, to produce newoutputs related to whether and to what extent water utility meters areproducing utility anomalies. In such manner, system 100 may be used tomonitor, over time, water utility meters.

Further, as explained in more detail below with reference to FIGS. 10,11, 12A, and 12B, systems and methods of the present arrangements andteachings may be used to provide information that prompts a waterutility company to remediate water meters producing utility anomalies(e.g., by reclassifying, adjusting, repairing, or replacing a waterutility meter at a particular location). Prior to that discussion,different exemplar configurations of the different componentsparticipating in the detection of utility anomalies is presented below.

FIG. 2A is a block diagram showing a data receptor 212, according to oneembodiment of the present arrangements, and depicting its relationshipswith certain inputs, outputs, and non-system devices associated withdata receptor 212. As with certain other structures and sub-systemsdescribed herein (e.g., with reference to FIGS. 2B-9B), data receptor212 and its related non-system components in the embodiment of FIG. 2Amay be thought of as a part of a larger system for detecting andreporting utility anomalies (e.g., system 100 of FIG. 1 ). Accordingly,a water meter 202, a water utility meter data repository 204, a waterutility billing data repository 206, a data receptor 212, and a datastorage device A 232, are substantially similar to their counterparts inFIG. 1 (i.e., water mater meter 102, water utility meter data repository104, water utility billing data repository 106, data receptor 112, anddata storage device A 132).

As shown in FIG. 2A, water meter 202 is communicatively coupled to waterutility meter data repository 204, which has stored therein waterutility data obtained from water meter 202. Each of water utility meterdata depository 204 and water utility billing data repository 206 (whichcontains water utility billing data stored in memory) is communicativelycoupled to data receptor 212 such that water utility meter data andwater utility billing data are received and organized at data receptor212 and, then during a subsequent instance in time, may be conveyed todownstream components. Water utility meter data repository 204 and/orwater utility billing data repository 206 may be operated by a utilitycompany and/or by a third party.

Once water utility billing data is received by data receptor 212, waterutility data (which may be thought of as the combination of waterutility meter data and water utility billing data) is conveyed to datastorage device A, where it is stored and made accessible to downstreamcomponents of the present systems for detecting and reporting utilityanomalies.

FIG. 2B is a block diagram showing a data receptor 212′, according to analternate embodiment of the present arrangements, and depicting itsrelationships with certain inputs, outputs, and non-system devicesassociated with data receptor 212′. A water meter 202′, a water utilitymeter data repository 204′, a water utility billing data repository206′, a first external data repository 108, a second external datarepository 210, a data receptor 212′, and a data storage device A 232′,are substantially similar to their counterparts in FIG. 1 (i.e., watermeter 102, water utility meter data repository 104, water utilitybilling data repository 106, first external data repository 108, secondexternal data repository 110, data receptor 112, and data storage deviceA 132).

As shown in FIG. 2B, each of water utility meter data repository 204′,water utility billing data repository 206, first external datarepository 208, and/or second external data repository 210, iscommunicatively coupled to data receptor 212′, such that data receptor212′ receives data streams associated with each data repository (i.e.,water utility meter data, water utility billing data, and externaldata). According to one embodiment of the present arrangements, datareceptor 212′ queries any of data repositories 204′, 206′, 208, and/or210, for new water utility billing data, water utility meter data,and/or one or more types of external data, which if present, isdelivered to data receptor 212′. According to another embodiment of thepresent arrangements, any of data repositories 204′, 206′, 208, and/or212′ is configured to notify data receptor 212′ when new water utilitydata and/or external data is available, and is further configured todeliver any such data to data receptor 212′. Then, data receptor 212′delivers water utility data and/or external data to data storage deviceA 232′ for storage

FIG. 3A is a block diagram showing a data transformer 314, according toone embodiment of the present arrangements, and depicting itsrelationships to certain inputs, outputs, and non-system devicesassociated with data transformer 304. FIG. 3A includes data transformer314, a first data storage device 332, and a second data storage device334, which are substantially similar to their counterparts in FIG. 1(i.e., data transformer 114, data storage device A 132, and data storagedevice B 134).

As shown in FIG. 3A, data storage device A 332 is communicativelycoupled to data transformer 314. In such manner, water utility datastored on data storage device A 332 is delivered as an input into datatransformer 314. Prior to delivery to data transformer 314, such waterutility data may be considered “original” water utility data.

Data transformer 314, then, is configured to carry out modification, ortransformation, of original water utility data, producing “modified”, or“transformed”, water utility data. In other words, data transformer 314modifies, or transforms, original data into a modified format thatpromotes downstream detection of utility anomalies by certain componentsof the systems of the present arrangements (e.g., system 100 of FIG. 1).

According to one embodiment of the present arrangements, datatransformer 314 examines and modifies original water utility data. Forexample, it may be useful for data transformer 314 to convert timestampsin water utility data from one time zone to another. As another example,it may be useful for data transformer 314 to convert a measurement ofwater volume consumption at a water meter from one unit of measure toanother. As yet another example, it may be useful for data transformer314 to remove data that is intended by the system to be excluded. Thesystems of the present arrangements contemplate any transformation ormodification of water utility data that facilitates downstream detectionof one or more anomalies.

According to another embodiment of the present arrangements, datatransformer 314 does not modify original water utility data, but insteadcarries out other actions related to original water utility datareceived from data storage device A 332. For example, data transformer314 may confirm that original water utility data is in an acceptableform, even if no modification of that data is required. In yet anotherembodiment of the present arrangements, original water utility data isprocessed by downstream components in system 100 of FIG. 1 in the formit is received, in which case data transformer 314 is not used. Forexample, it may be desirable not to use data transformer 314 if originalwater utility data is known to be in an acceptable form. The systems ofthe present arrangements contemplate any modification of water utilitydata (including no modification of water utility data) that promotesdetecting utility anomalies by systems of the present arrangements.

FIG. 3B is a block diagram showing a data transformer 314′, according toan alternate embodiment of the present arrangements, and depicting itsrelationships with certain inputs, outputs, and devices associated withdata receptor 212′. Data transformer 314′, a data storage device A 332′,and a data storage device B 334′, are substantially similar to theircounterparts in FIG. 3A (i.e., data transformer 314, data storage deviceA 332, and data storage device B 334).

Unlike the embodiment of FIG. 3A, data transformer 314′ in system 300′is also configured to modify and/or transform one more types of originalexternal data. For example, it may be desirable to convert timestamps inoriginal external data from one time zone to another. As anotherexample, it may be useful to confer a location address associated withoriginal external data into a standard format. As yet another example,it may be useful to remove certain types of external data, as suchexternal data may not be necessary for ultimately detecting a utilityanomaly.

According to another embodiment of the present arrangements, datatransformer 314′ examines, but does not modify, any portion of originalexternal data. For example, it may be desirable to confirm that originalexternal data is in an acceptable format, even if no modification of thedata is required. According to yet another embodiment of the presentarrangements, external data is processed in the form it is received bydata transformer 314′, such that data transformer 314′ is not used. Forexample, it may be desirable not to use data transformer 314′ iforiginal external data is known already to be in an acceptable format.

According to the embodiments of FIGS. 3A and 3B, data transformer 314and data transformer 314′, respectively, are configured to deliveroriginal and/or modified external data and/or water utility data to datastorage device B 334 and data storage device B 334′, respectively. Incertain embodiments of the present arrangements, data storage device A332 and/or data storage device A 332′ are the same as data storagedevice B 334 and/or data storage device 334′, respectively. In otherwords, both of the embodiments of FIGS. 3A and 3B contemplate use of adata transformer that receives original data from, and delivers modifieddata to, the same data storage device.

FIG. 4A is a block diagram showing a meter under-sizing detector 418,according to one embodiment of the present arrangements, and depictingits relationships with certain related inputs, outputs, and non-systemdevices. Meter under-sizing detector 418, a data storage device B 434,and a data storage device C 436 are substantially similar to theircounterparts in FIG. 1 (i.e., meter under-sizing detector 118, datastorage device B 134, and data storage device C 136).

As shown in FIG. 4A, meter under-sizing detector 418 is communicativelycoupled to data storage device B 434 and data storage device C 436.Meter under-sizing detector 418 searches water utility meter data and/orexternal data located on data storage device B 434 for water metersmatching a set of predefined criteria, preferably configured on datastorage device B 434, that indicate that a meter may be smaller than apreferred size for the location where it has been installed. As shown inFIG. 4A, the predetermined criteria includes “a recommended maximumvolume of water per meter” (i.e., a recommended maximum volume of waterper unit of time that a water meter (e.g. water meter 102 of FIG. 1 )may accurately measure based on its size). Meter under-sizing detector418 then records utility anomalies, preferably to data storage device C436.

Water utility meter data may be presented as time series datameasurements representing a sequence of values occurring at specifiedpoints in time. When meter time series measurements matching one or morepredefined thresholds are located, meter under-sizing detector 418records an anomaly on data storage device C 436 (to which meterunder-sizing detector 418 is communicatively coupled).

According to one embodiment of the present arrangements, meterunder-sizing detector 418 records an anomaly for each set of meter timeseries data measurements having at least one data point greater than therecommended maximum volume of water associated with a water meter. Forexample, if the recommended maximum volume of water per meter is 40gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 10, 20, and 10 gallons per minute, then noneof the data points is above the recommended maximum volume of water permeter, so no utility anomaly is recorded. As another example, if therecommended maximum volume of water per meter is 40 gallons per minute,and the data points in the time series measurements are equivalent to10, 15, 50, 20, and 10 gallons per minute, then one of the data pointsis above the recommended maximum volume of water per meter, andconsequently, one utility anomaly is recorded.

FIG. 4B is a block diagram showing a meter under-sizing detector 418′,according to an alternate embodiment of the present arrangements, andwhich depicts its relationships to certain related inputs, outputs, andnon-system devices. Meter under-sizing detector 418′, a data storagedevice B 434′, and a data storage device C 436′, are substantiallysimilar to their counterparts in FIG. 4A (i.e., meter under-sizingdetector 418, data storage device A 434, and data storage device B 436).

As shown in FIG. 4B, meter under-sizing detector 418′ is communicativelycoupled to data storage device B 434′ and data storage device C 436′.Meter under-sizing detector 418′ searches water utility meter data ondata storage device B 434′ for water meters matching a set of predefinedcriteria, preferably configured on data storage device B 434, thatindicate that a meter may be smaller than a preferred size for thelocation where it has been installed. As shown in FIG. 4B, thepredetermined criteria includes “a recommended maximum value of waterper meter” (as described above with reference to FIG. 4A), as well as a“minimum percentage of data points required” (i.e., a minimum percentageof data points greater than the recommended maximum volume of water permeter). To the extent that the embodiment of FIG. 4B must satisfy morecriteria than the embodiment of FIG. 4A, meter under-sizing detector418′ of FIG. 4B may be thought of as producing more accurate detectionof meter under-sizing than meter under-sizing detector 418 of FIG. 4A.In other words, the more (meaningful) constraints that are applied onthe utility data, which are ultimately subject to analysis by theanomaly-detecting modules 116 of FIG. 1 , preferably, the more accuratethe utility anomaly information is.

According to the embodiment of FIG. 4B, meter under-sizing detector 418′records an anomaly for each meter set of time series measurements havingat least the specified minimum percentage of data points greater thanthe maximum volume. In one example, if the recommended maximum volume ofwater per meter is 40 gallons per minute, and the data points in thetime series measurements are equivalent to 10, 15, 10, 20, and 10gallons per minute, and the minimum percentage of data points requiredis 25%, then none of the data points is above the recommended maximumvolume, so no anomaly is recorded. As another example, if therecommended maximum volume of water per meter is 40 gallons per minute,and the data points in the time series measurements are equivalent to10, 15, 50, 20, and 10 gallons per minute, and the minimum percentage ofdata points required is 25%, then 20% of the data points are above therecommended maximum volume, so no anomaly is recorded. As yet anotherexample, if the recommended maximum volume of water per meter is 40gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 50, 45, and 10 gallons per minute, and theminimum percentage of data points required is 25%, then 40% of the datapoints are above the adjusted recommended maximum volume, so one anomalyis recorded.

FIG. 4C is a block diagram showing a meter under-sizing detector 418″,according to another alternate embodiment of the present arrangements,and depicting its relationships with certain related inputs, outputs,and non-system devices. Meter under-sizing detector 418″, a data storagedevice B 434″, and a data storage device C 436″, are substantiallysimilar to their counterparts in FIG. 4B (i.e., meter under-sizingdetector 418′, data storage device B 434′, and data storage device C436′).

As shown in FIG. 4C, meter under-sizing detector 418″ is communicativelycoupled to data storage device B 434″ and data storage device C 436″.According to one embodiment of the present arrangements, meterunder-sizing detector 418″ searches water utility meter data and/orexternal data located on data storage device B 434″ for water metersmatching a set of predefined criteria, preferably configured on datastorage device B 434″, that indicate that a meter may be smaller than apreferred size for the location where it has been installed. As shown inFIG. 4C, multiple predetermined criteria may include a “recommendedmaximum volume of water per meter” (as described above with reference toFIGS. 4A and 4B), a “minimum percentage of data points required” (i.e.,minimum percentage of data points greater than the recommended maximumvolume), as well as a “minimum percentage of recommended maximum volumerequired” (i.e., the recommended maximum volume of water that a watermeter can accurately measure).

According to the embodiment of FIG. 4C, meter under-sizing detector 418″records an anomaly for each set of meter time series measurement havingat least the specified minimum percentage of data points required. Forexample, if the recommended maximum volume of water per meter is 40gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 10, 20, and 10 gallons per minute, and theminimum percentage of data points required is 25%, and the minimumpercentage of recommended maximum volume required is 80%, then none ofthe data points is above the recommended maximum volume, so no anomalyis recorded. As another example, if the recommended maximum volume ofwater per meter is 40 gallons per minute, and the data points in thetime series measurements are equivalent to 10, 15, 50, 20, and 10gallons per minute, and the minimum percentage of data points requiredis 25%, and the minimum percentage of recommended maximum volumerequired is 80%, then 20% of the data points are above the adjustedrecommended maximum volume, so no anomaly is recorded. As yet anotherexample, if the recommended maximum volume of water per meter is 40gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 38, 37, and 10 gallons per minute, and theminimum percentage of data points required is 25%, and minimumpercentage of recommended maximum volume required is 80%, then 40% ofthe data points are above the adjusted recommended maximum volume, soone anomaly is recorded.

As shown in FIG. 4C, meter under-sizing detector 418″ is communicativelycoupled to data storage device C 43′6′ such that utility anomaliesdetected by meter under-sizing detector 418″ are delivered to datastorage device C 436″ for storage, where this information may beaccessed by downstream components.

FIG. 5A is a block diagram showing a meter over-sizing detector 520,according to one embodiment of the present arrangements, and thatdepicts its relationship to certain related inputs, outputs, andnon-system devices. Meter over-sizing detector 520, a data storagedevice B 534, and a data storage device C 536 are substantially similarto their counterparts in FIG. 1 (i.e., meter over-sizing detector 120,data storage device B 134, and data storage device C 136).

As shown in FIG. 5A, meter over-sizing detector 520 is communicativelycoupled to data storage device B 534 and data storage device B 536.Meter over-sizing detector 520 searches water utility meter data and/orexternal data, located on data storage device B 534, for water metersmatching a set of predefined criteria that indicate that a meter may belarger than the preferred size for the location where it has beeninstalled. Such predetermined criteria may include a “recommendedminimum volume of water per meter” (i.e., the recommended minimum valueof water that each water meter can accurately measure based on the sizeof the meter). Water utility meter data and/or external data may be timeseries data measurements representing a sequence of values occurring atspecified points in time. When a set of meter time series measurementsmatching the criteria is located, meter over-sizing detector 520 recordsan anomaly on data storage device C 536.

According to one embodiment of the present arrangements, meterover-sizing detector 520 records an anomaly for each meter set of timeseries measurements having at least one data point smaller than therecommended minimum volume. For example, if the recommended minimumvolume of water per meter is 8 gallons per minute, and the data pointsin the time series measurements are equivalent to 10, 15, 10, 20, and 10gallons per minute, then none of the data points is below therecommended minimum volume, so no anomaly is recorded. As anotherexample, if the recommended minimum volume of water per meter is 8gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 5, 20, and 10 gallons per minute, then onedata point is below the recommended minimum volume, so one anomaly isrecorded.

FIG. 5B is a block diagram showing a meter over-sizing detector 520′,according to an alternate embodiment of the present arrangements, andthat depicts its relationship to certain related inputs, outputs, andnon-system devices. Meter over-sizing detector 520′, a data storagedevice B 534′, and a data storage device C 536′, are substantiallysimilar to their counterparts in FIG. 5A (i.e., meter over-sizingdetector 520, data storage device B 534, and data storage device C 536).

As shown in FIG. 5B, meter over-sizing detector 520′ is communicativelycoupled to data storage device B 534′ and data storage device C 536′.Meter over-sizing detector 520′ searches water utility meter data,located on data storage device B 534′, for water meters matching a setof predefined criteria that indicate a meter may be larger than thepreferred size for the location where it has been installed. As shown inFIG. 5B, such predetermined criteria includes a “recommended minimumvolume of water per meter” (as described above with reference to FIG.5A), as well as a “minimum percentage of data points required” (i.e., aspecified percentage of data points that must be below the minimumvolume of water per meter).

According to one embodiment of the present arrangements, meterover-sizing detector 520′ records an anomaly to data storage device C536 for each set of meter time series measurements having at least theminimum percentage of data points required. For example, if therecommended minimum volume of water per meter is 8 gallons per minute,and the data points in the time series measurements are equivalent to10, 15, 10, 20, and 10 gallons per minute, and the minimum percentage ofdata points required is 25%, then none of the data points is below therecommended minimum volume, so no anomaly is recorded. As anotherexample, if the recommended minimum volume of water per meter is 8gallons per minute, and the data points in the time series measurementsare equivalent to 10, 15, 5, 20, and 10 gallons per minute, and theminimum percentage of data points required is 25%, then 20% of the datapoints are below the recommended minimum volume, so no anomaly isrecorded. As yet another example, if the recommended minimum volume ofwater per meter is 8 gallons per minute, and the data points in the timeseries measurements are equivalent to 10, 15, 5, 7, and 10 gallons perminute, and the minimum percentage of data points required is 25%, then40% of the data points are below the recommended minimum volume, so oneanomaly is recorded to data storage device C 436′.

FIG. 5C is a block diagram showing a meter over-sizing detector 520″,according to another embodiment of the present arrangements, and thatdepicts its relationship to certain related inputs, outputs, andnon-system devices. Meter over-sizing detector 520″, a data storagedevice B 534″, and a data storage device C 536″, are substantiallysimilar to their counterparts in FIG. 5B (i.e., meter over-sizingdetector 520′, data storage device B 534′, and data storage device C536′).

As shown in FIG. 5C, meter over-sizing detector 520″ is communicativelycoupled to data storage device B 534″ and data storage device C 536″.Meter over-sizing detector 520″ searches water utility meter data,located on data storage device B 534, for water meters matching a set ofpredefined criteria that indicate a meter may be larger than thepreferred size for the location where it has been installed. Suchpredetermined criteria may include a “recommended minimum volume ofwater per meter” (as described above with reference to FIGS. 5A and/or5B), “a maximum percentage of recommended minimum volume required” (alsoknown as the “adjusted recommended minimum volume”), and a “minimumpercentage of data points required” (i.e., the specified percentage ofdata points that must be below the adjusted recommended minimum volume).

According to one embodiment of the present arrangements, meterover-sizing detector 520″ records an anomaly, to data storage device C536″, for each set of meter time series measurement having at least theminimum percentage of data points required. In one example, if therecommended minimum volume of water per meter is 8 gallons per minute,and the data points in the time series measurements are equivalent to10, 15, 10, 20, and 10 gallons per minute, and the maximum percentage ofrecommended minimum volume required is 110%, and the minimum percentageof data points required is 25%, then none of the data points is belowthe adjusted recommended minimum volume, so no anomaly is recorded. Asanother example, if the recommended minimum volume of water per meter is8 gallons per minute, and the data points in the time seriesmeasurements are equivalent to 10, 15, 5, 20, and 10 gallons per minute,and the maximum percentage of recommended minimum value required is110%, and the minimum percentage of data points required is 25%, then20% of the data points are below the adjusted recommended minimum volumeper meter, so no anomaly is recorded. As yet another example,recommended minimum volume of water per meter is 8 gallons per minute,and the data points in the time series measurements are equivalent to10, 15, 5, 7, and 10 gallons per minute, and the maximum percentage ofrecommended minimum value required is 110%, and the minimum percentageof data points required is 25%, then 40% of the data points are belowthe adjusted recommended minimum volume, so one anomaly is recorded todata storage device C 536″.

FIG. 6A is a block diagram showing a meter misclassification detector622, according to one embodiment of the present arrangements, and thatdepicts its relationships with certain related inputs, outputs, andnon-system devices. Meter misclassification detector 622, a data storagedevice B 634, and a data storage device C 636, are substantially similarto their counterparts in FIG. 1 (i.e., meter misclassification detector122, data storage device B 134, and data storage device C 136).

As shown in FIG. 6A, meter misclassification detector 622 iscommunicatively coupled to data storage device B 634 and data storagedevice C 636. Meter misclassification detector 622 searches waterutility meter and/or water utility billing data, and in certainembodiments of the present arrangements, external data, that is locatedon data storage device B 634, for water meters matching a set ofpredefined criteria that indicate that a meter may have beenmisclassified. Such predefined criteria include a “minimum percentilethreshold” (i.e., a percentile threshold above which a water utilitymeter is deemed to have been misclassified). One example of a watermeter that has been misclassified is a commercial water meter that hasbeen incorrectly classified as a residential water meter.

According to one embodiment of the present arrangements, water utilitymeter data and water utility billing data are presented as time seriesdata measurements representing a sequence of values occurring atspecified points in time. When a set of meter time series measurementsmatching the criteria is located, meter misclassification detector 622records an anomaly on data storage device C 636.

According to another embodiment of the present arrangements, metermisclassification detector 622 records an anomaly to data storage deviceC 636 for each meter with a set of time series data point measurementsabove the configured percentile threshold if the meter class in thebilling data does not match the detected meter class from the timeseries data measurements. For example, if meter misclassificationdetector is configured with a maximum percentile of 99%, then a meterwill be considered to have a detected class of commercial, and ananomaly will be recorded for each of the top 1% of data points, sortedby volume from highest to lowest values of volumes of water, having adetected class not matching commercial.

FIG. 6B is a block diagram showing a meter misclassification detector622′, according to an alternate embodiment of the present arrangements,and that depicts its relationships with certain related inputs, outputs,and non-system devices. Meter misclassification detector 622′, a datastorage device B 634′, and a data storage device C 636′, aresubstantially similar to their counterparts in FIG. 6A (i.e., metermisclassification detector 622, data storage device B 634, and datastorage device C 636).

As shown in FIG. 6B, meter misclassification detector 622′ iscommunicatively coupled to data storage device B 634′ and data storagedevice C 636′. Meter misclassification detector 622′ searches waterutility meter data and/or water utility billing data, located on datastorage device B 634, for water meters matching a set of predefinedcriteria that indicate that a meter may have been misclassified. Suchpredefined criteria in FIG. 6B includes a “minimum percentile threshold”(as described above with reference to FIG. 6A) and a “number of bedroomsat each property”.

According to one embodiment of the present arrangements, metermisclassification detector 622′ records an anomaly for each meter with aset of time series data point measurements above the minimum percentagethreshold if the meter class in the water utility billing data does notmatch the detected meter class from the time series data measurements,if the property where the associated water meter is installed is knownto have a specified number of bedrooms, and the water usage exceeds theamount of expected water usage for a property with that number ofbedrooms, multiplied by a configured percentage. For example, if metermisclassification detector 622′ is configured with a maximum percentileof 99%, then an anomaly will be recorded for each of the top 1% of datapoints, sorted by values of volume from highest to lowest, if theresidential property where the associated water meter is installed has 2bedrooms, and if the water usage is more than 200% of the expected waterusage for a property with 2 bedrooms.

FIG. 7A is a block diagram showing a meter tampering detector 724,according to one embodiment of the present arrangements, and thatdepicts its relationships with certain related inputs, outputs, andnon-system devices. Meter tampering detector 724, a data storage deviceB 734, and a data storage device C 736, are substantially similar totheir counterparts in FIG. 1 (i.e., meter tampering detector 122, datastorage device B 134, and data storage device C 136).

As shown in FIG. 7A, meter-tampering detector 724 is communicativelycoupled to data storage device B 734 and data storage device C 736.Meter-tampering detector 724 searches water utility meter data, waterutility billing data, and/or external data, located on data storagedevice B 734, for water meters matching a set of predefined criteriathat indicate that a water meter may have been tampered with. Suchpredetermined criteria include a “minimum percentage drop”, (i.e., aminimum drop in percentage values between consecutive time series datapoints for use of water at a particular location) and a “minimum numberof data points”.

Water utility meter data and water utility billing data may be presentedas time series data measurements representing a sequence of valuesoccurring at specified points in time. When a set of meter time seriesdata measurements matching the criteria is located, meter-tamperingdetector 724 records an anomaly on data storage device C 736.

According to one embodiment of the present arrangements, meter tamperingdetector 724 records an anomaly for each set of meter time seriesmeasurements including at least one percentage drop, from one data pointto the next data point, that exceeds the specified minimum percentagedrop, with at least the specified minimum number of data points, ifthere is known to be no change in occupancy at the property where theassociated water meter is installed. For example, if the data points inthe time series measurements are equivalent to 12, 15, 11, 0, 1, and 0kilogallons of water per month, and the specified minimum percentagedrop is 50%, and the specified minimum number of data points is 10, andthere has been no change in occupancy during the time period, then thereare not enough data points, so no anomaly is recorded. As anotherexample, if the data points in the time series measurements are 12, 15,11, 0, 1, and 0 kilogallons of water per month, and the specifiedminimum percentage drop is 50%, and the specified minimum number of datapoints is 2, and there has been no change in occupancy during the timeperiod, then the percentage drop between the third and fourth datapoints exceeds the minimum percentage drop and the number of data pointsexceeds the minimum number of data points, so one anomaly is recorded.As another example, if the data points in the time series measurementsare 12, 15, 11, 0, 1, and 0 kilogallons of water per month, and thespecified minimum percentage drop is 50%, and the specified minimumnumber of data points is 2, and there has been a change in occupancyduring the time period, then even though the percentage drop between thethird and fourth data points exceeds the minimum percentage drop and thenumber of data points exceeds the minimum number of data points, therewas a change in occupancy during the time period, so no anomaly isrecorded.

FIG. 7B is a block diagram showing a meter tampering detector 724′,according to an alternate embodiment of the present arrangements, andthat depicts its relationships with certain related inputs, outputs, andnon-system devices. Meter tampering detector 724′, a data storage deviceB 734′, and a data storage device C 736′, are substantially similar totheir counterparts in FIG. 7A (i.e., meter tampering detector 722, datastorage device B 734, and data storage device C 736).

As shown in FIG. 7B, meter-tampering detector 724′ is communicativelycoupled to data storage device B 734′ and data storage device C 736′.Meter-tampering detector 724′ searches water utility meter data, locatedon data storage device B 734′, for water meters matching a set ofpredefined criteria that indicate that a water meter may have beentampered with. Such predetermined criteria in FIG. 7B includes a“minimum percentage drop” and a “minimum number of data points” (both asdescribed above with reference to FIG. 7A), as well as a “maximumpercentage of minimum amount expected at each property” (i.e., a maximumpercentage of a minimum amount of water use at each property”), and a“number of residents at each property”.

Water utility meter data may be presented as time series datameasurements representing a sequence of values occurring at specifiedpoints in time. When a set of meter time series measurements matchingthe criteria is located, meter-tampering detector 724′ records ananomaly on data storage device C 736′. Meter tampering detector 724′records an anomaly for each set of meter time series measurementsincluding at least one percentage drop, from one data point to the nextdata point, that exceeds the specified minimum percentage drop, with atleast the specified minimum number of data points, if there is known tobe no change in occupancy at the property where the associated watermeter is installed, and if the average water consumption is less thanthe specified maximum percentage of the minimum amount of water useexpected for the number of residents at the property. For example, ifthe data points in the time series measurements are 12, 15, 11, 0, 1,and 0 kilogallons of water per month, and the specified minimumpercentage drop is 50%, and the specified minimum number of data pointsis 10, and there has been no change in occupancy during the time period,and the minimum amount of water consumption expected for the number ofresidents at the property is 20 kilogallons per month, and the specifiedmaximum percentage of the minimum amount expected for the number ofresidents at the property is 40%, then there are not enough data points,so no anomaly is recorded. As another example, if the data points in thetime series measurements are 12, 15, 11, 0, 1, and 0 kilogallons permonth, and the specified minimum percentage drop is 50%, and thespecified minimum number of data points is 2, and there has been nochange in occupancy during the time period, and the minimum amount ofwater consumption expected for the number of residents at the propertyis 20 kilogallons per month, and the specified maximum percentage of theminimum amount expected for the number of residents at the property is40%, then the percentage drop between the third and fourth data pointsexceeds the minimum percentage drop, the number of data points exceedsthe minimum number of data points, and the average water consumption isno more than 40% of the minimum amount expected for the number ofresidents at the property, so an anomaly is recorded. As anotherexample, if the data points in the time series measurements are 12, 15,11, 0, 1, and 0 kilogallons of water per month, and the specifiedminimum percentage drop is 50%, and the specified minimum number of datapoints is 2, and there has been a change in occupancy during the timeperiod, and the minimum amount of water consumption expected for thenumber of residents at the property is 20 kilogallons of water permonth, and the specified maximum percentage of the minimum amountexpected for the number of residents at the property is 40%, then eventhough the percentage drop between the third and fourth data pointsexceeds the minimum percentage drop and the number of data pointsexceeds the minimum number of data points, there was a change inoccupancy during the time period, so no anomaly is recorded.

FIG. 8A is a block diagram showing a meter under-registration detector826, according to one embodiment of the present arrangements, and thatdepicts its relationship to certain related inputs, outputs, andnon-system devices. Meter under-registration detector 826, a datastorage device B 834, and a data storage device C 836, are substantiallysimilar to their counterparts in FIG. 1 (i.e., meter under-registrationdetector 122, data storage device B 134, and data storage device C 136).

As shown in FIG. 8A, meter under-registration detector 826 iscommunicatively coupled to data storage device B 834 and data storagedevice C 836. Meter under-registration detector 826 searches waterutility meter data and/or external data located on data storage device B834 for water meters matching a set of predefined criteria that indicatethat a meter may be under-registering an amount of water consumed atthat location. Such predefined criteria include a “minimumunder-registration score”.

Water utility meter data may be presented as measurements datarepresenting a sequence of values occurring at specified points in time.When a set of meter time series measurements matching the criteria islocated, meter under-registration detector 826 records an anomaly ondata storage device C 836.

According to one embodiment of the present arrangements, meterunder-registration detector 826 calculates a correlation for each set oftime series measurements as a value between −1 and 1, and uses thisvalue, multiplied by −1, as the under-registration score for the timeseries measurements. Meter under-registration detector 826 is configuredwith the minimum under-registration score (as shown in FIG. 8A) abovewhich detected utility anomalies will be recorded.

According to one embodiment of the present arrangements, meterunder-registration detector 826 records an anomaly for each set of metertime series measurements having an under-registration score above thespecified minimum score. For example, if the specified minimumunder-registration score is 0.5, and the data points in the time seriesmeasurements are equivalent to 1, 1, 1, 1, and 1 kilogallons of waterper month, then a correlation is calculated as 0, so no anomaly isrecorded. As another example, if the specified minimumunder-registration score is 0.5, and the data points in the time seriesmeasurements are equivalent to 10, 8, 6, 4, and 2 kilogallons of waterper month, then a correlation is calculated as −1, and theunder-registration score is calculated as 1, so one anomaly is recorded.

FIG. 8B is a block diagram showing a meter under-registration detector826′, according to an alternate embodiment of the present arrangements,and that depicts its relationship to certain related inputs, outputs,and non-system devices. Meter under-registration detector 826′, a datastorage device B 834′, and a data storage device C 836′, aresubstantially similar to their counterparts in FIG. 8A (i.e., meterunder-registration detector 822, data storage device B 834, and datastorage device C 836).

According to the embodiment of FIG. 8B, meter under-registrationdetector 826′ records an anomaly for each meter set of time seriesmeasurements having an under-registration score above a “minimumunder-registration score” (as described above with reference to FIG.8A), and a “multiplier for each meter manufacturer”, where anunder-registration score is multiplied by the multiplier for each metermanufacturer based on the average rate of decay of a particular metermanufacturer's meter. For example, if the specified minimumunder-registration score is 0.5, and the data points in the time seriesmeasurements are equivalent to 1, 1, 1, 1, and 1 kilogallons of waterper month, and the specified multiplier for the meter manufacturer is0.25, then the correlation is calculated as 0, and theunder-registration score is calculated as 0, so no anomaly is recorded.As another example, if the specified minimum under-registration score is0.5, and the data points in the time series measurements are equivalentto 10, 8, 6, 4, and 2 kilogallons of water per month, and the specifiedmultiplier for the meter manufacturer is 0.25, then the correlation iscalculated as −1, and the under-registration score is calculated as0.25, so no anomaly is recorded. As another example, if the specifiedminimum under-registration score is 0.5, and the data points in the timeseries measurements are equivalent to 10, 8, 6, 4, and 2 kilogallons ofwater per month, and the specified multiplier for the meter manufactureris 0.75, then the correlation is calculated as −1, and theunder-registration score is calculated as 0.75, so one anomaly isrecorded.

FIG. 9A is a block diagram showing a data reporter 928, according to oneembodiment of the present arrangements, and that depicts itsrelationship to certain related inputs, outputs, and non-system devices.Data reporter 928, a user 930, and a data storage device C 936, aresubstantially similar to their counterparts in FIG. 1 i.e., datareporter 128, user 130, and data storage device C 136).

As shown in FIG. 9A, data reporter 928 is communicatively coupled todata storage device C 936. Data reporter 928 reads information aboututility anomalies stored on data storage device C 936 and displays alist of one or more possible utility anomalies associated with aparticular water meter. According to one embodiment of the presentarrangements, a user, such as a representative of the water utilitycompany or a water utility customer, connects to data reporter 928,preferably using a client device with a display screen, such as asmartphone, tablet, laptop computer, or desktop computer, to receive alist of one more possible utility anomalies on a display screenassociated with the client device.

In certain embodiments of the present teachings, other information ispresented on the display screen associated with data reporter 928. Forexample, a “certainty score”, providing a measurement, preferablyexpressed as a percentage between 0% and 100%, of the likelihood that ananomaly identified by the systems of the present arrangements actuallyis an anomaly. A certainty score may be used to rank a detected anomalyfrom lowest to highest (i.e., from least likely actually to be ananomaly to most likely actually to be an anomaly).

In certain embodiments of the present teachings, a certainty score iscalculated using data values associated with a particular anomaly (e.g.,a volume of water consumed at a location). In other embodiments of thepresent arrangements, a certainty score is calculated using data valuescomputed during anomaly detection (e.g., revenue associated with avolume of water).

A certainty score may be calculated by assigning, to a lower value, ascore of 0%, and assigning, to a higher value, a score of 100%, and theninterpolating certainty scores for data points that are between assignedvalues of 0% and 100%.

According to one embodiment of the present teachings, a lower value anda higher value that are assigned certainty scores of 0% and 100%,respectively, are theoretical values. For example, if a theoreticalminimum rate of water consumption at a location is 0 gallons/day, then acertainty score of 0% is assigned; and if a theoretical maximum rate ofwater consumption at a location is 750 gallons/day, then a certaintyscore of 100% is assigned and intermediate values for water consumptionbetween 0 gallons/day and 750 gallons/day are interpolated based onthese theoretical minimum and maximum values. In an alternate embodimentof the present teachings, a lower value and a higher value that areassigned certainty scores of 0% and 100%, respectively, are actual ormeasured values. But, the intermediate values may still be obtainedthrough interpolation as described above.

In other embodiments of the present teachings, regardless of whethertheoretical or actual values are used, certainty scores of 0% and/or100% are assigned to values that are not the lowest or highest values,respectively. For example, a middle data point value (i.e., not a lowestor highest data point value) may be assigned a certainty score of 0% or100%.

Once certainty scores of 0% and 100% have been assigned, interpolatingbetween these two scores is carried out to calculate a certainty scorefor any value that is between the lowest (i.e., assigned a certaintyscore of 0%) and highest (i.e., assigned a certainty score of 100%)values. Interpolating may be carried out by any method known to those ofskill in the art. By way of example, interpolating may be linear,logarithmic, asymptotic, or the like.

A map, showing geographical information about a location where utilityanomalies have been identified, may also be delivered to a data reportfor viewing by a user. The map may show a habitable structure that isassociated with a water utility meter on the location. Habitablestructure may indicate a footprint of a livable area inside the locationaddress. The map may also show or otherwise indicate the nature of useof areas external to the habitable structure. Examples of external areasinclude parking spaces, desert type landscaping, and green landscaping.In certain embodiments of the present arrangements, a third-party map(e.g., a satellite image map from Google maps) is used, and such a maptypically shows both habitable and external areas for a particularlocation address.

FIG. 9B is a block diagram showing a data reporter 928′, according to analternate embodiment of the present arrangements, and that depicts itsrelationship to certain related inputs, outputs, and non-system devices.Data reporter 928′, a user 930′, and a data storage device C 936′, aresubstantially similar to their counterparts in FIG. 9A (i.e., datareporter 928, user 930, and data storage device C 936).

According to the embodiment of FIG. 9B, data reporter 928 sendsinformation about possible utility anomalies to a user's client device,to which it is communicatively coupled. According to another embodimentof the present arrangements, a computer is communicatively coupled toData Storage Device C, and is also linked to a printer via a network orvia a direct connection. The computer instructs the printer to create aprinted report about one or more possible utility anomalies that havebeen detected.

FIG. 10 shows a user interface 1000, according to one embodiment of thepresent arrangements, depicting locations of property lots on a map anda dialog box that presents information about utility anomaliesassociated with at least one of those location addresses. Display screen1000 includes a lot 1002 a, a lot 1002 b, a lot 1002 c, a lot 1002 d, alot 1002 e, a lot 1002 f, a flag icon 1004, an external area 1006, ahabitable structure 1008, and a dialog box 1010.

User interface 1000 may be or include an electronic display screenassociated with a client device that is capable of receiving informationabout utility anomalies, either directly or indirectly, from a systemfor detecting utility anomalies (e.g., system 100 of FIG. 1 ). Accordingto preferred embodiment of the present arrangements, a client deviceassociated with user interface is communicatively coupled to a datareporter (e.g., data reporter 928 of FIG. 9A), for receiving informationabout utility anomalies from systems of the present arrangements (e.g.,system 100 of FIG. 1 ).

The map depicted on user interface 1000 shows a series of locations,i.e., lots 1002 a, 1002 b, 1002 c, 1002 d, 1002 e, and 1000 f, arrangedalong a cul-de-sac. Preferably, one or more of lots 1002 a-100 f areassociated with a water utility meter that measures water consumption ateach location.

As shown in FIG. 10 , flag icon 1004 abuts and/or otherwise emanatesfrom a boundary of lot 1002 d. Preferably, use of flag icon 1004 in suchmanner at lot 1002 d indicates that one or more utility anomalies havebeen detected on a particular location address, lot 1002 d, by systemsof the present arrangements.

External area 1006 and habitable structure 1008 are also shown in lot1002 d. Habitable structure 1008, at this location address, mayrepresent a residence, a commercial building, or any other buildingstructure associated with water consumption on lot 1002 d. Likewise,external area 1006, at this location address, may be used to showcertain other features that provide additional information about wateruse on lot 1002 d. For example, though not shown on FIG. 10 , externalarea 1006 may show one or more pools on lot 1002 d, indicating thatwater use at that location may be higher than would otherwise beexpected.

Dialog box 1010 provides additional information, in narrative form,about one or more utility anomalies associated with lot 1002 d (i.e.,the location identified by flag icon 1004). As shown in FIG. 10 , dialogbox 1010 may provide a location address and a list of utility anomaliesassociated with the location address. Other information may optionallybe included, such as a percentage certainty score associated with one ormore utility anomalies, costs savings, and other information that may berelevant to a water utility company, a water utility customer, and/or athird-party worker who has been hired to investigate and/or repair oneor more utility anomalies at lot 1004 d. In such manner, informationabout utility anomalies is promptly conveyed to a user, who may thentake steps to remediate such problems.

In certain embodiments of the present arrangements, color-coding may beused to convey certain information on display screen 1000 about utilityanomalies and/or a particular location where utility anomalies have beendetected. For example, flag icon 1004 and/or a location (e.g., lot 1002d) may be color coded to convey a measure of certainty score (e.g., useof the color red may indicate a certainty score greater than 90%). Asanother example, flag icon 1004 and/or other objects on a map may bepresented in a particular color to identify utility anomalies thatrequire immediate attention. As another example, flag icon 1004 and/orother objects on a map may be presented in a particular color toidentify a location where systems of the present arrangements did notidentify any anomaly. The present systems for detecting and reportingutility anomalies contemplate any such use of color coding on a userinterface to convey information about one or more utility anomaliesand/or a location associated with one or more utility anomalies.Further, the present systems for detecting and reporting utilityanomalies contemplate the use of other techniques and features thathighlight utility anomaly information, such as flashing text and/orexclamation marks.

FIG. 11 shows a user interface 1100, according to another embodiment ofthe present arrangements, depicting locations on a map and a dialog boxthat presents information about utility anomalies associated with atleast one of those location addresses. Lots 1102 a, 1102 b, 1102 c, 1102d, 1102 e, and 1002 f, an external area 1006, and a habitable structure1108, are substantially similar to their counterparts in FIG. 1100(i.e., lots 1002 a, 1002 b, 1002 c, 1002 d, 1002 e, and 1000 f, andhabitable structure 1008). FIG. 11 also shows a flag icon 1104, whichunlike flag icon 1004 of FIG. 10 , is depicted as selectable by a user(i.e., as indicated by dashed lines shown inside flag icon 1104).Further, FIG. 11 shows a dialog box 1112, which lists certaininformation about one or more utility anomalies detected at lot 1104 d.

According to one embodiment of the present arrangements, selection, by auser, of selectable flag icon 1104 on a user interface, prompts a userdevice to present, on the user interface, dialog box 1112. Dialog box1112 may then present certain information about one or more one or moreutility anomalies associated with lot 1002 d By way of example, dialogbox 1112 may present a list of utility anomalies, cost savingsassociated with remediating one or more of utility anomalies, percentagecertainty score associated with one or more utility anomalies,recommended actions to take regarding one or more utility anomalies,date of completion of remediation, status of remediation efforts,further testing required at water meter location, and individualworker(s) assigned to remediation of one or more utility anomalies. Insuch manner, the systems of the present arrangements provide immediateguidance to a water utility company, a customer, and/or a third-partyworker regarding the nature, status, and effects water meters that maybe defective in a manner that produces utility anomalies.

Use of selectable flag icon 1104 provides certain advantages. Forexample, a selectable flag icon allows user to request information to bepresented on an as-needed basis. As another example, on maps identifyingutility anomalies at multiple locations using multiple selectable flagicons, any on flag icon may be selected by a user to provide informationabout utility anomalies at that particular location.

FIG. 12A shows a dialog box 1214, according to one embodiment of thepresent arrangements, for providing entry of instructions regarding oneor more utility anomalies identified at a location (e.g., lot 1004 d ofFIG. 10 ). Unlike the dialog boxes of FIGS. 10 and 11 , dialog box 1214of FIG. 12 is provided so that a user may input instruction regardingutility anomalies at a location in dialog box 1214. In other words,dialog box 1214 is configured to receive inputs from a user. By way ofexample, a representative of a water utility company may provideinstructions, in dialog box 1214, that may later be accessed by a workerwho has been requested to investigation and/or remediate one or moreutility anomalies. By way of example, instructions and/or informationregarding steps to be taken to repair or replace a water meter, locationof a water meter on property, potential hazards at a location, watermeter identification number, water meter manufacturer, water meter type,installation date of a water meter, and/or a premise number of alocation or lot (i.e., an identification number of location asdesignated by a utility company), may be input by one user into dialogbox 1214, for later reference by another user, or that use.

According to one embodiment of the present arrangements, dialog box 1214is presented on client device user interface in response to an actiontaken by a user. For example, dialog box 1214 may be presented upon userclicking hyperlinked text (e.g., hyperlinked text from dialog box 1100of FIG. 11 ) or a selectable icon (e.g., selectable flax icon 1104 ofFIG. 11 ).

FIG. 12B shows a dialog box 1214′ and a dialog box 1216, according toanother embodiment of the present arrangements, for providing entriesand instructions related to remediation of one or more utilityanomalies. Dialog box 1214′ is substantially similar to its counterpartin FIG. 12A (i.e., dialog box 1214). Dialog box 1216, according to oneembodiment of the present arrangements, is presented, upon selection bya user, on a user device display screen for entry of remarks or commentsregarding remediation of one or more utility anomalies.

For example, a worker who has been hired to remediate utility anomaliesat a location may use dialog box 1214′ to receive certain instructionsabout the utility anomalies. Then, before, during, or following theworker's investigation of and/or attempt to remediate utility anomalies,the worker may use dialog box 1216, on a user device, to input furtherinstructions, observations, conclusions, and/or comments related toutility anomalies. For example, a worker may input information such astools to bring, spare parts to bring, meter type to bring forreplacement, and meter size to bring for replacement, status of attemptsat remediation, further information about externals areas or habitablestructures associated with utility anomalies.

Further information may also be transmitted to a user interface (e.g.,display screen 1100 of FIG. 11 ). For example, notice that remediationis complete at a location, notice that remediation is delayed at alocation, and cost saving expected or resulting from remediation, may betransmitted. Further, related information may also be presented in otherforms. For example, an estimated value of costs savings at a locationaddress resulting from remediation may be presented on a billingstatement associated with that location address.

FIG. 13 is a flowchart showing certain salient steps of a process 1300,according to one embodiment of the present teachings, for utilityintervention. “Utility intervention” may be thought of as certain stepstaken by a utility company to detect, report, and/or remediate one ormore utility anomalies associated with a water meter at a particularlocation. Preferably, utility intervention includes using the presentsystems for detecting and reporting utility anomalies (i.e., system 100of FIG. 1 ).

Process 1300 begins with a step 1302, which includes obtaining utilitydata from a utility data repository. Utility data may be thought of asany type of data associated with detecting utility anomalies in watermeters (e.g., water utility meter data, water utility billing data,and/or one or more types of external data).

Obtaining in step 1302 may include obtaining water utility meter datafrom a water utility meter data repository (e.g., water utility meterdata repository 104 of FIG. 1 ), obtaining water utility billing datafrom a water utility meter billing depository (e.g., water utilitybilling data repository 106 of FIG. 1 ), and/or obtaining one or moretypes of external data from one or more types of external datarepositories (e.g., first external data repository 108 and secondexternal data repository 110 of FIG. 1 ).

Preferably, utility data obtained in step 1302 is received by a datareceptor (e.g., data receptor 112 of FIG. 1 ), which then organizesand/or advances such utility data for storage in a data storage device(e.g., data storage device A 132 of FIG. 1 ), where it is conveyed forfurther processing by systems of the present teachings (e.g., system 100of FIG. 1 ).

In certain embodiments of the present teachings, obtaining in step 1302includes modifying and/or transforming utility data into an acceptableform. In other words, prior to advancing to step 1304, external data maybe in an original form that require modification into a modified, oracceptable, form that is more amenable to processing in downstream stepsof process 100. Preferably, such modification is carried out by a datatransformer (e.g., data transformer 114 of FIG. 1 ). After modificationinto an acceptable form, resulting modified utility data may be storedon a data storage device (e.g., data storage device B 134 of FIG. 1 ),where it is accessible for further processing in subsequent steps.

Next, a step 1304 includes detecting, using at least one type ofanomaly-detecting module installed on a server (e.g., meter under-sizingdetector module 118, meter over-sizing detector module 120, metermisclassification detector module 122, and/or meter tampering detectormodule 124 of FIG. 1 ), one or more utility anomalies of at least onetype, and a location address of one or more of the utility anomalies(e.g., a location address for lot 1002 d of FIG. 10 ). As explainedabove with reference to FIGS. 1 and 4A-8B, each anomaly-detecting moduleis configured with certain predetermined thresholds that are used, whenaccessing utility data (e.g., utility data delivered from and/oraccessed in data storage device B 134 of FIG. 1 ), to carry out certaincalculations to detect the presence of one or more utility anomalies.Step 1304 may also include producing a list of one or more utilityanomalies detected. Such list may be stored on a data storage device(e.g., data storage device C 136 of FIG. 1 ). Associated data andinformation may also be stored on the same, or a different, data storagedevice.

Next, a step 1306 includes calculating, using the server, an amount offinancial savings for at least one of the utility anomalies, if theutility anomaly was remedied or addressed, so that the utility anomalywas no longer deemed an anomaly by the server. The present teachingsrecognize that further data and information may be input into, orotherwise accessed by, certain components related to a system fordetecting and reporting utility anomalies, for further processing ofmodified utility data. For example, a “financial savings module” may beemployed by systems of the present teachings to carry out calculationsof cost savings associated with remediating one or more utilityanomalies detected. In other words, if a water utility meter anomaly isdetected, systems of the present teachings may be configured to provideand estimate of how much cost savings a customer and/or a water utilitycompany can save and/or earn if the water utility meter anomaly would nolonger be deemed an anomaly by systems of the present teachings.

Next, a step 1308 includes computing, using the server, a certaintyscore for at least one of the utility anomalies. The present teachingsrecognize that components of systems of the present teachings (e.g.,system 100 of FIG. 1 ) and/or additional components not shown herein(e.g., a certain score module) may be used by a server to calculate apercentage score reflecting certainty that a water utility meter anomalydetected by systems of the present teachings is, in fact, an anomaly.Such information may be useful to a water utility company in determiningwhat further steps should be taken. For example, if a water utilitymeter anomaly is detected, but a certainty score associated with theanomaly is relatively low, then the water utility company may beprompted to take certain further steps (e.g., an on-site investigation)to confirm the existence of the anomaly before taking remediatingaction. Further, a certainty score may also be used to adjust a level ofcost savings estimated for remediating a water utility meter dataanomaly.

Data and information related to a certain score may then be delivered toa data storage device (e.g., data storage device C 136 of FIG. 1 ) forstorage and downstream conveyance for further processing and/orconveyance.

Next, a step 1310 includes conveying the certain score for at least oneof the utility anomalies, information about the type of one ore more ofthe utility anomalies, information about the type of one or more of theutility anomalies, and the location address of one ore more of theutility anomalies, and the location address of one or more of theutility anomalies, from the server to a client device, which iscommunicatively coupled to the server. The client device may include anintegrated or otherwise attached user interface that displays suchinformation in a dialog box (i.e., dialog box 1112 of FIG. 11 ). Suchinformation, identifying the location address of one or more of theutility anomalies, a certainty score associated with one or more of theutility anomalies, and other information related to one or more of theutility anomalies, may prompt further action to investigate and/orremediate a utility anomaly. The present teachings recognize that byproviding such information quickly to a water utility company, acustomer, or a third party, remediation efforts may be carried outalmost immediately, providing additional opportunities for cost andwater savings.

Next, a step 1312 includes displaying, on a user interface of the clientdevice, a map depicting a geographical area that identifies, using aflag, at least one of the location addresses on the map of one or moreof the utility anomalies, the type of at least one of the utilityanomalies, the certainty score for each of the utility anomalies, and/oran amount of financial savings associated with each of the utilityanomalies. For example, as shown in the map depicted on display screen1000 of FIG. 10 , a flag icon (e.g., flag icon 1004 of FIG. 10 ) toidentify a location where an anomaly has been detected (e.g., a locationaddress associated with lot 1002 d of FIG. 10 ). A dialog box (e.g.,dialog box 1006 of FIG. 10 ) may be displayed on or near the map suchthat information about the flagged property is provided in the dialogbox, which may include a location address, a list of utility anomaliesdetected, a certainty score associated with one or more utilityanomalies, as well as other related information about the utilityanomalies. In certain embodiments of the present teachings, a flag iconmay be selectable (e.g., selectable flag icon 1104 of FIG. 11 ).Selecting the flag icon may then prompt the appearance of a dialog boxdisplaying certain information about one or more utility anomaliesidentified by systems of the present teachings (e.g., dialog box 1106 ofFIG. 11 ).

The present teachings recognize that subsequent steps may be taken tofurther address the existence of one or more utility anomalies that havebeen detected by systems of the present teachings. For example, a dialogbox may be shown on a client device display screen that provides aregion for entry of instructions associated with utility anomalies(e.g., dialog box 1214 of FIG. 12A). Further, another dialog box may bepresented on a display screen that provides a region for entry ofinstructions for a user (e.g., a third-party worker) who will or hasinvestigate utility anomalies detected at a particular location (e.g.,dialog box 1216 of FIG. 12B).

Although illustrative embodiments of the present arrangements andteachings have been shown and described, other modifications, changes,and substitutions are intended. Accordingly, it is appropriate that theappended claims be construed broadly and in a manner consistent with thescope of the disclosure, as set forth in the following claims.

1. A method of utility intervention, said method comprising: obtainingutility data from a utility data repository; detecting, using at leastone type of anomaly-detecting module installed on a server, one or moreutility anomalies of at least one type and a location address of one ormore of said utility anomalies; calculating, using said server, anamount of financial savings for at least one of said utility anomaliesif said utility anomaly was remedied or addressed so that said utilityanomaly was no longer deemed an anomaly by said server; computing, usingsaid server, a certainty score for at least one of said utilityanomalies and wherein said certainty score is a measure of certaintythat said utility anomaly, obtained from said detecting, is indeed ananomaly, and not a false positive result; conveying said certainty scorefor at least one of said utility anomalies, information about said typeof one or more of said utility anomalies, and said location address ofone or more of said utility anomalies from said server to a clientdevice, which is communicatively coupled to said server; and displaying,on a display screen of said client device, a map depicting ageographical area that identifies, using a flag presented as aselectable icon, at least one of said location address on said map ofone or more of said utility anomalies and said type of at least one ofsaid utility anomalies presenting information, upon user's selection ofsaid selectable icon for said flag, regarding remediation of saidutility anomaly that includes said location address on said map of oneor more of said utility anomalies, said type of at least one of saidutility anomalies, said certainty score for each of said utilityanomalies, and amount of said financial savings associated with each ofsaid utility anomalies.
 2. The method of utility intervention of claim1, further comprising transforming, using a data transformer module,said utility data obtained from said utility data repository to put inacceptable form, which allows said detecting to be carried out.
 3. Themethod of utility intervention of claim 2, wherein in acceptable form,said location addresses in said utility data are converted to sameformat, and/or timestamps in said utility data are converted to timevalues in same time zone.
 4. The method of utility intervention of claim3, further comprising storing, in a data storage device, said utilitydata in acceptable form.
 5. The method of utility intervention of claim4, further comprising conveying said utility data in acceptable formfrom said data storage device to said server to carry out saiddetecting.
 6. The method of utility intervention of claim 1, whereinduring said detecting, said anomaly-detecting module used includes onemodule chosen from a group comprising meter under sizing detector, meterover sizing detector, meter misclassification detector, meter tamperingdetector, and meter under registration detector.
 7. The method ofutility intervention of claim 6, wherein said meter under sizingdetector detects whether a utility meter at said location address has asize smaller than a predetermined size for said utility meter, whereinsaid meter over sizing detector detects whether said utility meter atsaid location address has a size larger than said predetermined size forsaid utility meter, wherein said meter misclassification detectordetects whether said utility meter at said location address ismisclassified, wherein said meter tampering detector detects whethersaid utility meter at said location address has been tampered with, andwherein said meter under registration detector detects whether saidutility meter at said location address is under registering amount ofuse of said utility at said location address; and wherein said utilitymeter measures amount of use of said utility at said location address.8. The method of utility intervention of claim 1, wherein said conveyingincludes sending said certainty score for at least one of said utilityanomalies and said information about said type of one or more of saidutility anomalies, from said server to a memory and then from saidmemory to a data reporter.
 9. The method of utility intervention ofclaim 1, wherein said location address conveys information aboutboundary of a habitable area and information about external area that isoutside said habitable area.
 10. The method of utility intervention ofclaim 9, wherein said external area conveys qualitative informationabout nature of use of said location address and/or about extent ofconsumption of said utility due to nature of external area, and whereinsaid qualitative information allows user of said client device to deduceextent of consumption of said utility in said habitable area.
 11. Themethod of utility intervention of claim 10, wherein said flag ispresented as a selectable icon on said user interface of said clientdevice.
 12. (canceled)
 13. The method of utility intervention of claim12, wherein said presenting information includes providing instructionfor remediating said utility anomaly.
 14. The method of utilityintervention of claim 12, further comprising presenting an input region,on said user interface, for receiving remediation information of saidutility anomaly.
 15. The method of utility intervention of claim 14,further comprising transmitting said remediation information, through atext or an electronic mail address associated with said location addressand that presents one or more available times to perform remediation atsaid location address.
 16. The method of utility intervention of claim14, further comprising receiving one or more selected available times,for remediation at said location address, from said text or saidelectronic mail address associated with said location address.
 17. Themethod of utility intervention of claim 14, further comprising conveyingsaid selected available times, to carry out remediation at locationaddress, to a remediation entity or worker.
 18. The method of utilityintervention of claim 17, further comprising transforming display offlag from a selectable icon to a non-selectable icon and/or transmittingnotice, through a text or an electronic email address associated withsaid location address, that said remediation entity or worker hascompleted said remediation at said location address.
 19. The method ofutility intervention of claim 18, further comprising transmitting anestimated value for cost savings, at said location address, resultingfrom said remediation at said location address, to said client device.20. The method of utility intervention of claim 18, further comprisingproviding, in a billing statement associated with said location address,an estimated value of cost saving, at said location address, resultingform said remediation at said location address.